Tutorial stats 011422: Difference between revisions
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=!!UNDER CONSTRUCTION!!= |
=!!UNDER CONSTRUCTION!!= |
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== Tutorial Session == |
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Recording Download: [https://megcore.nih.gov/MEG/ClubMEG_Carver&Stout_MNE&AFNI-GroupAnalysisTutorial_012122.mp4 MNE & AFNI Group Analysis Tutorial (mp4)] |
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== Afni Prep == |
== Afni Prep == |
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=== Statistical Processing in AFNI === |
=== Statistical Processing in AFNI === |
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#! /bin/bash |
#! /bin/bash |
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# First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset |
# First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset |
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# Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp |
# Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp |
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# Example: One Sample T-test on alpha band log ratio of face power and shape power |
# Example: One Sample T-test on alpha band log ratio of face power and shape power |
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# Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution. |
# Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution. |
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# First make a list of subjects for test: |
# First make a list of subjects for test: |
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ls *_7_13_*lograt*.HEAD > alpha_list |
ls *_7_13_*lograt*.HEAD > alpha_list |
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# Then run one sample T-test |
# Then run one sample T-test |
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3dttest++ -setA `cat alpha_list` -prefix alpha_ttest |
3dttest++ -setA `cat alpha_list` -prefix alpha_ttest |
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# Optional: add effect size to output |
# Optional: add effect size to output |
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3dMean -prefix alpha_mean `cat alpha_list` |
3dMean -prefix alpha_mean `cat alpha_list` |
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3dMean -sd -prefix alpha_std `cat alpha_list` |
3dMean -sd -prefix alpha_std `cat alpha_list` |
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3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b' |
3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b' |
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3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc |
3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc |
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3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc |
3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc |
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# Could do a paired T-test between faces and shapes instead of log-ratio |
# Could do a paired T-test between faces and shapes instead of log-ratio |
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ls *_7_13_*shape*.HEAD > alpha_shape_list |
ls *_7_13_*shape*.HEAD > alpha_shape_list |
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ls *_7_13_*face*.HEAD > alpha_face_list |
ls *_7_13_*face*.HEAD > alpha_face_list |
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3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest |
3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest |
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# Can add covariates to T-tests. |
# Can add covariates to T-tests. |
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== MNE Python == |
== MNE Python == |
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=== Install MNE Python === |
=== Install MNE Python === |
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conda install mamba -y |
conda install mamba -y |
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mamba create -n tutorial_stats conda-forge::mne conda-forge:datalad -y |
mamba create -n tutorial_stats conda-forge::mne conda-forge::datalad -y |
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conda activate tutorial_stats |
conda activate tutorial_stats |
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=== Download the data === |
=== Download the data === |
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git clone ------- data repo |
git clone ------- data repo |
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cd ---- |
cd ---- |
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Latest revision as of 12:58, 23 March 2023
!!UNDER CONSTRUCTION!!
Tutorial Session
Recording Download: MNE & AFNI Group Analysis Tutorial (mp4)
Afni Prep
Install Afni on computer:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html
Statistical Processing in AFNI
#! /bin/bash # First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset # Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp # Example: One Sample T-test on alpha band log ratio of face power and shape power # Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution. # First make a list of subjects for test: ls *_7_13_*lograt*.HEAD > alpha_list # Then run one sample T-test 3dttest++ -setA `cat alpha_list` -prefix alpha_ttest # Optional: add effect size to output 3dMean -prefix alpha_mean `cat alpha_list` 3dMean -sd -prefix alpha_std `cat alpha_list` 3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b' 3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc 3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc # Could do a paired T-test between faces and shapes instead of log-ratio ls *_7_13_*shape*.HEAD > alpha_shape_list ls *_7_13_*face*.HEAD > alpha_face_list 3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest # Can add covariates to T-tests. # Can do mixed-effects ANOVAs and other fancier stuff
MNE Python
Install MNE Python
conda install mamba -y mamba create -n tutorial_stats conda-forge::mne conda-forge::datalad -y conda activate tutorial_stats
Download the data
Will Update soon with appropriate paths etc.
git clone ------- data repo cd ---- datalad get ./*
Additional MEG Stats Links
https://mne.tools/stable/auto_tutorials/stats-sensor-space/10_background_stats.html https://eelbrain.readthedocs.io/en/stable/getting_started.html https://neuroimage.usc.edu/brainstorm/Tutorials/Statistics https://www.fieldtriptoolbox.org/tutorial/eventrelatedstatistics/